There have been many incidents of marine mammal strandings that have been locationally and temporally coincident with naval exercises during which active sonar systems were used. There has been a public perception that sound generated by the active sonar systems causes tissue damage to marine mammals, tending to cause the marine mammals to beach themselves. As is known, beaching has caused death in some marine mammals.
As a result of the public's negative perception of naval active sonar systems, the United States Navy has invested a large amount of time and money conducting scientific research into the effects of low frequency underwater sound on humans and on marine mammals, which has resulted in an environmental impact statement and ongoing investigations into the causes of these marine mammal incidents. Public controversy exists over the use of the naval active sonar system and other mid-frequency active sonar systems.
It would be useful to provide an active sonar system that is generally harmless to marine mammals. However, such as system must still meet the Navy's mission requirements for detection, localization, and/or classification of underwater targets.
A hypothesis has been generated regarding the impact of naval mid-frequency active sonar systems upon marine mammals. In particular, it has been hypothesized that the particular frequency modulated active sound signals transmitted by the mid-frequency active sonar systems share similar sound characteristics with natural sound signals generated by killer whales. The hypothesis further suggests that when the marine mammals detect one or more of such frequency modulated sound signals, in particular, sound signals generated by sonar systems aboard a plurality of navel ships maneuvering to triangulate upon an underwater target, the marine mammals may perceive the sound signals to be generated by a pod of killer whales hunting prey. This perception by the affected marine mammals may elicit a behavioral “flight” response to flee the area, which can result in strandings.
It has been further hypothesized that the beaked whale species is particularly affected by these particular mid-frequency frequency modulated sound signals and triangulation maneuvers. As is known, approximately one quarter of the world's eighty whale and dolphin species belongs to the family of beaked whales (Ziphiidae), however, since many of these marine mammals favor a deep water habitat, study and knowledge of these marine mammals and their behaviors is relatively new.
While some have hypothesized that marine mammals are physiologically damaged by the sound emitted by the naval active sonar systems, necropsies of beaked whales that have beached themselves after naval active sonar exercises have shown no conclusive evidence of hemorrhaging or other physiological damage resulting from the active sonar systems, in particular, resulting from high sound pressure levels associated with the active sonar systems. Also, there has been no evidence that the marine mammals have experienced the bends (outgasing of nitrogen into the blood stream due to rapid depressurization from rapid depth change). Therefore, the strandings of beaked whales are believed to be a result of a behavior in response to the particular frequency modulated sound signals generated by naval active sonar systems.
Referring to FIGS. 1-1B, a sound recording of an Orca (killer whale) is presented in a variety of graphical formats. Referring first to FIG. 1, a graph 10 has a horizontal axis in units of time in seconds and a vertical axis in units of amplitude in arbitrary units. A curve 12 is a time waveform indicative of an exemplary sound signal generated by an Orca. The time waveform 12 has been normalized to contain peak amplitudes that reside within amplitude bounds of +/−1.0. It can be seen from this example of sound generated by the Orca that this waveform has a duration of approximately one second. The curve 12 has first and a second portion 12a, 12b, respectively, which are not necessarily characteristic of all sounds generated by Orcas.
Referring now to FIG. 1A, a graph 20 has a horizontal axis in units of frequency in Hertz and a vertical axis in units of amplitude in decibels. A curve 22, which is a power spectrum, was computed by calculating a single Fourier Transform record over the entire duration of the Orca transmission shown in waveform 12 of FIG. 1. The power spectrum curve 22 has been normalized to have a peak of zero decibels. A peak 22a is associated with background ocean noise. A peak 22b is representative of sound generated by the Orca.
Referring now to FIG. 1B, a graph 30 includes a horizontal scale in units of frequency and a vertical scale in units of time in seconds. The graph 30 is a spectrogram representative of a sound signal generated by an Orca in accordance with the time waveform 12 of FIG. 1. A first curve 32a spans a time period of approximately one second. A second curve 32b spans a time period of approximately 0.5 seconds. A third curve 32c spans a time period of approximately 0.3 seconds. The three curves 32a-32c correspond to the time waveform 12 of FIG. 1 and to the power spectrum 22 of FIG. 1A. Each one of the three curves 32a-32c has a frequency that changes with respect to time, becoming higher in frequency as time progresses. Therefore, each one of the curves 32a-32c is a frequency sweep (also referred to here as a chirp or a frequency modulated sound signal) that tends to increase in frequency with time.
A third dimension is represented in the graph 30 by an intensity of each one of the curves 32a-32c, wherein a darker portion of the curves 32a-32c is representative of a greater sound power.
The spectrogram 30 presented in FIG. 1B is normalized (in accordance with the power spectrum of FIG. 1A) to contain a peak power level of 0 dB. A floor of −40 dB was employed to limit the dynamic range of the levels of the spectrogram 30 to 40 dB. The 40 dB dynamic range is employed to allow a depiction of the spectrogram 30 that could be visually interpreted over a 256-value color map.
The curves 32a-32c represent a complex sound signal generated by the Orca that includes harmonically spaced spectral components that change frequency as a function of time over a broad range of frequencies.
It is presumed that only one Orca contributed to the sound signal represented by the curves 12, 22, 32a-32c of FIGS. 1, 1A, and 1B, respectively. This presumption is based on characteristics of the three curves 32a-32c of FIG. 1B, which have similar frequency versus time slopes. These three curves 32a-32c also begin concurrently. The likelihood of independent mammals producing the sound represented by the curves 32a-32c, which have concurrent starting times, similar frequency versus time slopes, and are harmonically spaced, is deemed to be very low.
Harmonic components evident in FIG. 1B appear to be naturally produced by the Orca and are not likely the result of signal recording or signal processing artifacts. If harmonic content were introduced in the recording process rather than by the Orca, the harmonic content would likely be a result of clipping a signal. However, clipping tends to generate harmonics at odd multiples of a fundamental component. This is not the case in FIG. 1B. The curves 32a-32c correspond to both even and odd harmonics.
It will be appreciated that a frequency sweep represented by the curves 32a-32c is within a mid frequency band of about 1 kHz to 5 kHz. In discussion below, some characteristics of the frequency sweep represented by the curves 32a-32c are compared with characteristics of an exemplary waveform employing a frequency sweep, which is representative of a waveform that can be used by a conventional mid-frequency active sonar system.
Conventional active sonar systems can employ a variety of transmit waveforms that can be selected based on a desired objective or mission. Exemplary waveforms include, but are not limited to, a single frequency tonal pulse, a linear frequency modulation waveform (LFM), and a Linear-Period Modulated (LPM) waveform (also sometimes referred to as a Hyperbolic Frequency Modulation (HFM) waveform or a Logarithmic Phase Modulation waveform).
The usage of the HFM waveform provides a variety of benefits including mitigating the degradation of the waveform compression generated by correlation processing when receiving a Doppler distorted waveform as an input. As is known, the HFM sound signal optimizes a so-called Frequency-Modulation Law for use on Doppler-affected sound signals that have a large time-bandwidth product. Presuming an instantaneous period to be linear optimizes the Frequency-Modulation Law. Slope and y-intercept parameters govern the HFM waveform as can be seen in equations below. The HFM waveform can be written as:
                                          s            ⁡                          (              t              )                                =                                    A              ⁡                              (                t                )                                      ⁢                          cos              ⁡                              [                                                                            2                      ⁢                      π                                        b                                    ⁢                                      ln                    ⁡                                          (                                              1                        +                                                                              b                                                          T                              0                                                                                ⁢                          t                                                                    )                                                                      ]                                                    ⁢                                  ⁢                  0          ≤          t          ≤                      T            0                          ⁢                                  ⁢                              A            ⁡                          (              t              )                                =                      Amplitude            ⁢                                                  ⁢            Window            ⁢                                                  ⁢            Function                          ⁢                                  ⁢                                            T              ⁡                              (                t                )                                      =                                                            T                  0                                +                                  b                  ⁢                                                                          ⁢                  t                                            =                              Instantaneous                ⁢                                                                  ⁢                Period                                              ⁢                                          ⁢          b          =                      Slope            ⁢                                                  ⁢            of            ⁢                                                  ⁢            Instantaneous            ⁢                                                  ⁢            Period                          ⁢                                  ⁢                              T            0                    =                      Y            -                          Intercept              ⁢                                                          ⁢              of              ⁢                                                          ⁢              Instantneous              ⁢                                                          ⁢              Period                                                          Eq        .                                  ⁢                  (          1          )                    
Referring now to FIG. 2, a graph 40 includes a horizontal axis in units of time in seconds and a vertical scale in units of amplitude. A curve 42 is representative of an exemplary time waveform associated with a sound signal generated by a conventional sonar system. The time waveform 42 has a carrier frequency of approximately 3500 Hz, a time duration of approximately one second, and a bandwidth of approximately 1000 Hz. A time duration of approximately one second and bandwidth of approximately 1000 Hz are chosen for the exemplary conventional waveform 42 based on observations from the spectrogram 30 of FIG. 1B. A center frequency of 3500 Hz is chosen to place the exemplary HFM waveform 42 in the mid frequency band.
At the particular time scale shown, individual cycles of the waveform 42 are not visible. However, a curved amplitude envelope 42a, representative of an exemplary amplitude (or temporal) window (or weighting function), described more fully below, is visible. The time waveform 42 has been normalized to contain a peak amplitude that resides within amplitude bounds of +/−1.0. The time waveform 42 has a frequency modulated (FM) frequency sweep, which is also not visible at the time scale shown. The FM sweep can be a hyperbolic FM (HFM) frequency sweep. As used herein, the term “chirp” is used to describe a signal that changes frequency with time from a start frequency to a stop frequency. The FM time waveform 42 can be generated using either an upward or a downward frequency sweep as a function of time. In this example, an upward frequency sweep characteristic was employed. An upward frequency sweep was chosen for the example HFM waveform to be consistent with the behavior observed in the spectrogram generated from the recording of the Orca transmission.
The exemplary HFM time waveform 42 of FIG. 2 has a temporal amplitude weighting function depicted by the curved envelope function of curve 42a. The temporal window function 42a represents a Gaussian window function employing a value of alpha that is 1.5.
Performance comparisons made below between various man-made waveforms (which were built using identical time-bandwidth products) are based on autocorrelation and ambiguity functions. Some of the performance parameters that are derived from the autocorrelation and ambiguity functions include, but are not limited to:                An expected maximum side lobe interference level in the time (or range) domain        An expected maximum side lobe interference level in the frequency (or Doppler) domain        An optimal theoretical range resolution (governed by the half power points) of a waveform correlated with itself under zero Doppler conditions        An optimal theoretical Doppler resolution (governed by the half power points) of a resultant waveform generated by taking the Fourier transform of the time waveform correlated with itself under zero Doppler conditions        An indication of Doppler tolerance of a waveform as the waveform undergoes Doppler distortions imparted on it by targets having unknown relative radial velocities.        
In order to establish meaningful performance comparisons between the various man-made waveforms discussed herein, a single performance parameter can be held to a fixed value, while the remaining performance parameters can be allowed to vary. The performance parameters that are allowed to vary can then be compared for each waveform in order to identify benefits and/or problems associated with a selected waveform when compared to the exemplary HFM waveform 42 of FIG. 2.
The fixed performance parameter used herein is a maximum allowable temporal side lobe interference level relative to the maximum peak associated of a replica correlation (i.e. a correlation of a waveform with a replica of itself, which can be an autocorrelation when the replica and the waveform are the same). This performance parameter was fixed to the constant level of −28 dB.
The output of a replica correlation process for HFM waveform having a flat envelope (not shown) results in a maximum temporal side lobe interference level of −13 dB relative to the peak of the correlated waveform. In order to establish the maximum temporal side lobe interference level at −28 dB relative to the peak of the correlated waveform, a temporal amplitude weighting function was required, which is represented by the amplitude envelope 42a. A Gaussian window function utilizing an alpha value of 1.5 results in a maximum temporal side lobe interference level of −28 dB relative to the peak of the correlated waveform.
Referring now to FIG. 2A, a graph 50 includes a horizontal axis in units of frequency in Hertz and a vertical scale in units of decibels. A curve 52 is a power spectrum representative of an exemplary conventional HFM sound signal generated by a conventional sonar system in accordance with the time waveform 42 of FIG. 2. This power spectrum was computed by calculating a single Fourier Transform record over the entire duration of the waveform depicted in FIG. 2. The power spectrum curve 52 has been normalized to 0 dB, such that all levels are plotted relative to a maximum power level of 0 dB.
This curve 52 is representative of the above-described time waveform 42 having a hyperbolic FM frequency sweep, a Gaussian amplitude window, a carrier frequency of approximately 3500 Hz, a time duration of approximately one second, and a bandwidth of approximately one thousand Hz.
The transmit time waveform 42 (chirp) of FIG. 2 has favorable transmit properties associated with sonar hardware employed by many conventional active sonar systems. The chirp 42 also has many favorable characteristics relative to sonar receiver signal processing. Unlike a linear FM chirp waveform, the HFM chirp waveform has a power level that is inversely proportional to frequency. However, it will be recognized that the above-described windowing tends to affect the ability to see this frequency dependent power characteristic in the power spectrum 52, when plotted using a decibel scale. The frequency dependent power level, which is apparent in the power spectrum 52, results from both the frequency dependent power characteristic of the HFM waveform as well as the above-described window function.
Referring now to FIG. 2B, a graph 60 includes a horizontal scale in units of frequency and a vertical scale in units of time in seconds. The graph 60 is a spectrogram representative of an exemplary conventional HFM signal generated by a conventional sonar system in accordance with the time waveform 42 of FIG. 2 and to the power spectrum 52 of FIG. 2A. A curve 62 spans a time period of approximately one second. The curve 62 has a frequency that changes with respect to time, becoming higher in frequency as time progresses, and having a frequency sweep rate and a frequency span similar to that of the curves 32a, 32b, 32c of FIG. 1B, which were generated by an Orca.
A third dimension is represented in the graph 60 by an intensity of the curve 62, wherein a darker portion of the curve 62 is representative of a greater sound power.
Background noise was added to the time waveform 42 of FIG. 2 to ensure similar background noise characteristics are present when comparing the Orca waveforms of FIG. 1 to the man-made waveforms described herein. The spectrogram 60 was computed from a sum of the resultant background noise plus HFM waveform signal. The noise background that was added to the man made HFM waveform 42 was obtained by sampling the ocean sounds in the Orca recordings when no Orca transmissions were present.
Like the spectrogram 30 of FIG. 1B, the spectrogram 60 of FIG. 2B was normalized to have a peak power level of 0 dB. A floor of −40 dB was employed to limit the dynamic range of the spectrogram levels to 40 dB. This 40 dB dynamic range was employed to allow a depiction of the spectrogram that could be visually interpreted over a 256-value color map.
The curve 62 is indicative of a narrowband tonal component that sweeps up in frequency as a function of the time over a finite duration pulse length. It can be seen that the curve 62, which is representative of a signal generated by a convention HFM sonar system, has characteristics (e.g., modulation, time duration, bandwidth, center frequency, sweep rate) similar to the curves 32a-32c of FIG. 1B, which are representative of sound generated by an Orca. The similarity provides evidence that supports the above-described hypotheses that naval vessels employing these types of chirp HFM signals can result in beaching of certain marine mammals due to a perception by the marine mammals that killer whales are nearby.
The Orca signal, which is represented by time the time waveform 12 of FIG. 1, by the power spectrum curve 22 of FIG. 1A, and by the spectrogram curves 32a-32c of FIG. 1B and also the conventional sonar HFM signal, which is represented by the time waveform 42 of FIG. 2, by the power spectrum 52 of FIG. 2A, and by the spectrogram curve 62 of FIG. 2B, reside in the mid-frequency band used by conventional sonar systems. As used herein, the mid-frequency band is from about 1 kHz to about 5 kHz. Signals 12, 42 both have generally the same pulse lengths (time durations) of approximately one second. Signals 12 and 42 contain frequency content that resides within the mid frequency band as described above. Also, signals 12 and 42 have tonal components, which sweep upward in frequency over a one kHz bandwidth at a similar rate of about one kHz per second.
Although the Orca sound signal 12 is not identical to the conventional HFM sound signal 42, they may appear similar enough that a marine mammal, which hears the man-made HFM chirp signal 42, may react with a flight response, resulting in a beaching.
Even a simple human auditory evaluation of the conventional HFM sound signal 42, when compared to the Orca generated sound signal 12, demonstrates that the naturally produced Orca sound signal 12 and the man-made HFM chirp 42 sound similar to each other. The similarity in the perception of the sound between these two signals further suggests that marine mammals could misinterpret the man-made HFM chirp signals to be sound generated by an Orca.
From discussion above, it will be understood that the time waveform 42 of FIG. 2 is generated with an upward frequency sweep characteristic. However, FM chirp waveforms used in a conventional active sonar system can be generated with either an upward or downward frequency sweep characteristic. The downward sweep characteristic has been observed to be generated at some times by some killer whales. It is not unreasonable to assume that a downward sweep waveform would elicit the same negative response in some marine mammals.
As described above, particular waveforms are used by active sonar systems based on particular objectives or missions, for example, detection, localization, tracking, or classification of a target. The particular waveform selected for a particular objective or mission has a major impact on the overall system performance. Waveforms used to generate sound signals in a sonar system are characterized by a variety of characteristics that include, but are not limited to a modulation, a pulse duration, a center frequency, a bandwidth, a frequency sweep rate, a temporal amplitude window function, and a signal energy (or peak sound pressure level).
Frequency modulated chirp waveforms having a variety of signal characteristics (e.g. modulation, pulse duration, center frequency, frequency sweep type and rate, bandwidth, amplitude weighting) are used by modem sonar systems in the detection, localization, tracking and classification problem. Because the various performance parameters of the system are inter-related, the waveforms are generated based on practical considerations. It should be understood that, since the performance parameters are inter-related, optimizing a waveform for one particular performance parameter often has undesirable (negative) effects on other performance parameters, so that the selection of a waveform is essentially a trade-off exercise.
Some of the issues considered when selecting a sonar waveform include, but are not limited to, the following considerations:                Can the hardware support a continuous chirp waveform, or is it better suited for short duration transmission of discrete frequency bands?        Can the transmitter support complex waveforms?        Are there bandwidth limitations of the transmitter?        What is the extent of waveform transmission time dictated by the transmitter?        What is the desired maximum detection range for the system?        What level of gain is desired from the matched filter processing?        What is the desired range resolution?        What is the desired Doppler (or relative speed) resolution?        What are the effects of the medium that the designer/operator wishes to mitigate (i.e. waveform distortion due to a dispersive medium)?        Does the operator care more about side lobes levels in Doppler or in range?        Does the operator wish to mitigate the degradation of the pulse compression processing due to Doppler distortions?        
As described above, evaluation of the ability of a sound signal to detect a target and to identify a range to a target can be performed using autocorrelation and associated ambiguity functions. Autocorrelation includes correlating a waveform with an identical copy of itself (i.e. a replica). Autocorrelation is the correlation of the waveform with itself and is a more restrictive function of a general cross correlation. The autocorrelation function executed for a waveform provides an optimal theoretical gain provided by a matched filter (i.e., waveform compression) for the waveform in the presence of uncorrelated noise. A comparison of a computed ratio of signal energy to noise energy prior to the correlation processing versus following the correlation processing provides the gain associated with the waveform compression (which is related to the time-bandwidth product). A time delay associated with a location of a peak of an output signal generated by the correlation processing provides a calculated time delay for the received sound signal, which corresponds to a range to the target.
Use of the autocorrelation function provides a measurement of matched filter performance and demonstrates a corresponding theoretical range determination accuracy (localization) that can be obtained with a transmitted sound signal having any signal characteristics, e.g., modulation, time duration, center frequency, bandwidth, and signal energy.
An auto-ambiguity function (or more simply, an ambiguity function) allows the matched filter performance to be studied in two dimensions (time delay and Doppler frequency shift resulting from relative movement between the active sonar system and the target). The ambiguity function can provide an evaluation of an ability of the transmitted sound signal having particular signal characteristics to resolve a target's range as a function of the target's relative radial speed (i.e. Doppler frequency). An ambiguity diagram in the frequency/Doppler dimension it demonstrates an ability of a waveform to identify a target's relative velocity. To this end, an ambiguity function uses as a reference a stored waveform associated with a transmitted sound signal. This stored waveform is the impulse response of the matched filter. The impulse response of the matched filter is convolved with several time-delayed and time distorted versions of the reference signal to produce correlated output waveforms at each relative speed condition.
Detection of targets moving at unknown radial velocities relative to the sonar becomes difficult as a result of distortion (e.g., Doppler shift) of the received waveform. The received waveform becomes distorted as a result of relative motion between the sonar and the target. The relative motion of the target imparts either a compression effect or an elongation effect on the received waveform relative to what was transmitted. Whether the effect is elongation or compression is related to the target's direction relative to the sonar system. The waveform distortion results in a degradation of the matched filter performance, since the received waveform is no longer an ideal match to what was transmitted (ignoring all other effects). This degraded matched filter performance means that the correlation process provides less than the designed signal processing gain, which has a direct effect on the ability of the system to detect a signal in the presence of noise.
Some conventional active sonar systems measure and use an own ship's speed in Doppler nullification processing in an attempt to account for the expected Doppler shift in a sound signal that echoes from a target with relative radial velocity. However, most times the actual relative radial speed of the target is (initially) unknown when both the target and the ship are moving as opposed to the scenario of the moving ship and stationary target. The ambiguity function provides a means by which to compute an ambiguity diagram. The ambiguity diagram provides a means by which a waveform designer can evaluate several performance parameters at the same time in both range (time) and Doppler (frequency) dimensions. As the designer makes changes to the waveform characteristics, resulting performance effects on expected system performance (at the matched filtering stage) can be examined in both dimensions. In this manner, a waveform can be designed that meets performance requirements in one dimension with an acceptable loss of performance in the other dimension.
Many active sonar systems attempt to minimize performance degradation associated with correlation processing that can result from a target having an unknown relative radial speed. One method that can be used to minimize the performance degradation is to choose a transmitted signal that has characteristics that tend to provide a relatively low degree of performance degradation (relative to the correlation processing) in the presence of Doppler distorted received sound signals. Such a transmitted signal is commonly referred to as a “Doppler tolerant waveform.” The HFM waveform described above is one such Doppler tolerant waveform.
Another method that can be used to minimize the performance degradation (associated with the matched filter processing) is to use a plurality of Doppler shifted replicas of the stored waveform associated with the transmitted sound signal in a corresponding plurality of parallel matched filters, (e.g., a plurality of parallel cross-correlations), each of which is therefore tuned to a specific Doppler shift (i.e., target relative radial velocity). In this case a Doppler tolerant waveform is not a necessity. The received sound signal is processed in parallel with each one of the plurality of replicas of the transmitted sound signal. By selecting the replica that results in the best correlation output, the target can be detected by a peak in the correlated output, a range to the target can be identified by a time delay of the peak, and also a relative speed can be identified as a result of determining a frequency associated with the replica that produced the best correlator output. However, it will be understood that use of the parallel processing channels requires a substantial increase in receiver processing load.
Lastly, another technique used to that can be used to minimize performance degradation (associated with the matched filter processing) is to utilize a set of multiple waveform types in a transmit sequence. Each waveform type is built to optimize its ability to provide a distinct piece of information. For example, a transmission sequence might utilize four FM transmissions followed by a single frequency tonal waveform transmission. The wide band waveforms (FM) are built with a large time bandwidth product to result in optimal waveform compression and hence optimize the correlator output and detection process in the time/range dimension. The single frequency tonal can have characteristics that optimize the detection process in the Doppler dimension by minimizing the frequency content and thus optimizing the ability to determine the Doppler shift (i.e. relative radial speed) of the target. A feedback mechanism allows the information gained from Doppler transmissions to provide information back to the Doppler nullification processing to optimize the correlation output (i.e. detection processing) by adjusting the replica waveform accordingly. In this case, it will be understood that use of the multiple waveforms requires an increase in receiver processing, an increase in system transmit design, and that for each Doppler transmission/receive cycle a range/bearing information transmission/receive cycle is sacrificed.
Two common waveforms employed by active systems for range/bearing evaluations are linear FM waveforms and hyperbolic FM waveforms. Each signal has its benefits and drawbacks, which are known to those of ordinary skill in the art.